The prevalence of sarcasm in multimodal dialogues on the social platforms presents a crucial yet challenging task for understanding the true intent behind online content. Comprehensive sarcasm analysis requires two key aspects: Multimodal Sarcasm Detection (MSD) and Multimodal Sarcasm Explanation (MuSE). Intuitively, the act of detection is the result of the reasoning process that explains the sarcasm. Current research predominantly focuses on addressing either MSD or MuSE as a single task. Even though some recent work has attempted to integrate these tasks, their inherent causal dependency is often overlooked. To bridge this gap, we propose MuVaC, a variational causal inference framework that mimics human cognitive mechanisms for understanding sarcasm, enabling robust multimodal feature learning to jointly optimize MSD and MuSE. Specifically, we first model MSD and MuSE from the perspective of structural causal models, establishing variational causal pathways to define the objectives for joint optimization. Next, we design an alignment-then-fusion approach to integrate multimodal features, providing robust fusion representations for sarcasm detection and explanation generation. Finally, we enhance the reasoning trustworthiness by ensuring consistency between detection results and explanations. Experimental results demonstrate the superiority of MuVaC in public datasets, offering a new perspective for understanding multimodal sarcasm.
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Traffic prediction plays a pivotal role in contemporary web technologies, motivating various intelligent web services such as route planning and remote traffic management. Many recent proposals that target deep learning for traffic prediction solely leverage historical traffic observations to predict future ones. However, traffic prediction is always susceptible to different factors such as road networks and social events, exhibiting different modalities. Most existing methods focus on a single modality, failing to capture the comprehensive traffic patterns among various factors, resulting in sub-optimal performance. Web-sourced geo-images, e.g., satellite imagery, encompass comprehensive contextual information and offer an effective way to represent diverse modalities. To unleash the power of such geo-images, we propose VisionST, a Vision-augmented Spatial-Temporal Neural Network, which coordinates cross-modal traffic prediction with interactive geo-image encoding. To bolster resilience against highly intricate and overlapping traffic patterns, VisionST features a visual semantic extraction mechanism and a pattern-guided aggregation mechanism. The former extracts node-level visual tokens and node-to-node visual relation patterns from geo-referenced images. The latter generates relation patterns that encompass visual, spatial, and temporal aspects, constraining nodes to interact with these relation patterns for contextual information interaction. Extensive experiments on real large-scale datasets offer insight into the effectiveness of the proposed solutions, showing that VisionST consistently outperforms state-of-the-art baselines.
With the rapid proliferation of short-video platforms and content-driven social networks, sequential recommendation models capable of accurately capturing user interests have become increasingly crucial. Among these, Transformer-based sequential recommendation models have gained widespread adoption due to their superior ability. The positional encoding (PE) in Transformer architectures serves to incorporate positional information into sequences. However, relying solely on original absolute positional information may be insufficient for sequential recommendation models. In contrast, the dwell time after interactions (i.e., the time intervals between consecutive user interactions) provides a more accurate reflection of users' emotional responses and evolving interests. Despite its significance, this aspect has often been overlooked in existing works. To fully utilize this information, our work introduces an adaptive PE method, termed TSAPE (Temporal-Series-Aware Positional Encoding). This approach introduces an innovative modeling of the sequence of time intervals between user interactions, rather than the numerical values of the intervals themselves, thereby capturing real-time feedback on user interests and integrating it with conventional PE mechanisms. Furthermore, we employ multiple layers of one-dimensional convolutional networks and attention mechanisms to endow the features with adaptive capabilities across various time interval scenarios. This enables TSAPE to more accurately capture sequential positional information at any given moment. By enhancing the sequential order information of interactions, TSAPE significantly improves the accuracy of next-item recommendations. We seamlessly integrated our method into several Transformer-based sequential recommendation models and conducted comparisons with state-of-the-art sequential recommendation approaches and widely-used PE methods. The results demonstrate that the integration of TSAPE consistently outperforms the original backbone models and other SOTA methods. The SASRec model integrated with TSAPE achieves an average improvement of 15.61% across three evaluation metrics on four benchmark datasets. Our code has been made publicly available at https://github.com/rongbo-qi/TSAPE_Rec.
Graph Neural Networks (GNNs) have become a pivotal framework for modeling graph-structured data, enabling a wide range of applications from social network analysis to molecular chemistry. By integrating large language models (LLMs), text-attributed graphs (TAGs) enhance node representations with rich textual semantics, significantly boosting the expressive power of graph-based learning. However, this synergy introduces critical vulnerabilities in both topology and text. Although specialized attack methods have been designed for each of these aspects, no work has yet unified them into a comprehensive approach. In this work, we propose the Interpretable Multi-Dimensional Graph Attack (IMDGA), a human-centric framework orchestrating multi-level perturbations across graph structure and textual features. IMDGA utilizes three tightly integrated modules to craft attacks that balance interpretability and impact, enabling a deeper understanding of Graph-LLM vulnerabilities. Through rigorous theoretical analysis and comprehensive empirical evaluations on diverse datasets and architectures, IMDGA demonstrates superior interpretability, attack effectiveness, stealthiness, and robustness compared to existing methods. By exposing these underexplored semantic vulnerabilities, our work offers valuable insights for improving Graph-LLM resilience. Our code is available at https://github.com/bwfan-bit/IMDGA.
The increasing sensitivity to human aspects in autonomous systems engineering calls for principled approaches to embed ethical and normative concerns into their behaviour. Indeed, recent research has focused on expressing and validating sets of social, legal, ethical, empathetic, and cultural (SLEEC) concerns as rules. However, existing work is limited to rule specification or verification, leaving the problem of semantic-preserving operationalisation of ethical rules in autonomous systems largely unaddressed. For this purpose, we provide an operational solution for ethical-aware autonomous systems, applied to the realm of multi-service robots. Specifically, we devise a principled approach, named CO-SLEEC (Context-Oriented SLEEC), connecting the normative setting of SLEEC rules to context-oriented programming (COP). CO-SLEEC enables runtime adaptation while preserving the semantics of SLEEC rules during robot task execution. It features two reusable Python libraries for (i) parsing SLEEC rules into contextual elements for operationalising them, and (ii) connecting the operational model to the Robot Operating System (ROS), respectively. We evaluate our implementation for correctness, efficiency, and maintainability over multi-service assistive robot scenarios.
MAMA-Memeia! Multi-Aspect Multi-Agent Collaboration for Depressive Symptoms Identification in Memes
PDF ↗Over the past years, memes have evolved from being exclusively a medium of humorous exchanges to one that allows users to express a range of emotions freely and easily. With the ever-growing utilization of memes in expressing depressive sentiments, we conduct a study on identifying depressive symptoms exhibited by memes shared by users of online social media platforms. We introduce RESTOREx as a vital resource for detecting depressive symptoms in memes on social media through the Large Language Model (LLM) generated and human-annotated explanations. We introduce MAMA-Memeia, a collaborative multi-agent multi-aspect discussion framework grounded in the clinical psychology method of Cognitive Analytic Therapy (CAT) Competencies. MAMA-Memeia improves upon the current state-of-the-art by 7.55% in macro-F1 and is established as the new benchmark compared to over 30 methods.
Class imbalance has been extensively studied in single-view scenarios; however, addressing this challenge in multi-view contexts remains an open problem, with even scarcer research focusing on trustworthy solutions. In this paper, we tackle a particularly challenging class imbalance problem in multi-view scenarios: long-tailed classification. We propose TMLC, a Trusted Multi-view Long-tailed Classification framework, which makes contributions on two critical aspects: opinion aggregation and pseudo-data generation. Specifically, inspired by Social Identity Theory, we design a group consensus opinion aggregation mechanism that guides decision-making toward the direction favored by the majority of the group. In terms of pseudo-data generation, we introduce a novel distance metric to adapt SMOTE for multi-view scenarios and develop an uncertainty-guided data generation module that produces high-quality pseudo-data, effectively mitigating the adverse effects of class imbalance. Extensive experiments on long-tailed multi-view datasets demonstrate that our model is capable of achieving superior performance.
Human-Centered Supervision for Sentiment Analysis in Telugu: A Systematic Inquiry Beyond Accuracy
PDF ↗Sentiment analysis for low-resource languages remains challenging in an era where interpretability, human alignment, and fairness are increasingly non-negotiable aspects of modern machine learning systems. These challenges stem both from the scarcity of annotated data and from the resulting difficulty of conducting reliable, human-interpretable analyses that go beyond predictive accuracy. Telugu, one of the primary Dravidian languages with over 96 million speakers, is not an exception. In this work, we first introduce TeSent, a large-scale Telugu sentiment classification dataset annotated with sentiment labels and human-selected rationales from multiple native speakers. This resource enables the study of rationale-based supervision for aligning models with human reasoning in this low-resource setting. We fine-tune five transformer-based models with and without rationale supervision and evaluate them on classification performance, explanation quality, and social bias. To facilitate controlled fairness evaluation, we additionally construct TeEEC, an evaluation corpus for Telugu sentiment analysis. Our results show that incorporating human rationales consistently improves alignment and often leads to holistic gains in predictive performance. We further provide extensive analysis of multi-facade explanation quality and fairness, offering insights into the broader effects of alignment-oriented supervision in resource-scarce language contexts.
The interpretation of implicit meanings is an integral aspect of human communication. However, this framework may not transfer to interactions with Large Language Models (LLMs). To investigate this, we introduce the task of Implicit Information Extraction (IIE) and propose an LLM-based IIE pipeline that builds a structured knowledge graph from a context sentence by extracting relational triplets, validating implicit inferences, and analyzing temporal relations. We evaluate two LLMs against crowdsourced human judgments on two datasets. We find that humans agree with most model triplets yet consistently propose many additions, indicating limited coverage in current LLM-based IIE. Moreover, in our experiments, models appear to be more conservative about implicit inferences than humans in socially rich contexts, whereas humans become more conservative in shorter, fact-oriented contexts. Our code is available at https://github.com/Antonio-Dee/IIE_from_LLM.
Theory of Mind (ToM)—an understanding of the mental states of others—is a key aspect of human social intelligence, yet, chatbots and LLM-based social agents do not typically integrate it. In this work, we demonstrate that LLMs that explicitly use ToM get better at dialogue, achieving goals more effectively. After showing that simply prompting models to generate mental states between dialogue turns already provides significant benefit, we further introduce ToMAgent (ToMA), a ToM-focused dialogue agent. ToMA is trained by pairing ToM with dialogue lookahead to produce mental states that are maximally useful for achieving dialogue goals. Experiments on the Sotopia interactive social evaluation benchmark demonstrate the effectiveness of our method over a range of baselines. Extensive analysis shows that ToMA exhibits more strategic, goal-oriented reasoning behaviors, which enable long-horizon adaptation, while maintaining better relationships with their partners. Our results suggest a step forward in integrating ToM for building socially intelligent LLM agents.
Can generative agents be trusted in multimodal environments? Despite recent advances, agents remain limited in their ability to reason about safety, coherence, and trust across modalities. We introduce a reproducible simulation framework to evaluate generative agents in three aspects: (1) safety improvement over time via iterative plan revision in multimodal scenarios; (2) detection of unsafe activities across social contexts; and (3) social dynamics, measured through interaction and acceptance rates. These multimodal agents are evaluated using metrics that quantify plan revisions and unsafe-to-safe conversions. Experiments show that while agents detect direct multimodal contradictions, they often fail to align local revisions with global safety, achieving only a 55% success rate in correcting unsafe plans. We release a dataset of 1,000 multimodal plans, yielding more than 600,000 simulation steps. Notably, 45% of unsafe actions are accepted when paired with misleading visual cues, revealing a strong tendency to overtrust visual content. Code is available at https://github.com/AdonaiVera/X-CASE
Spoken language models (SpeechLMs) are increasingly real-time conversational actors. Yet many culturally consequential aspects of spoken interaction are not primarily lexical. Across sociolinguistics, linguistic anthropology, and conversation analysis, meaning emerges through how talk is produced and coordinated—prosody, timing, turn-taking, overlap, backchannels, and repair—within situated speech events. A transcript can be semantically correct yet interactionally inappropriate because many culture-bearing signals are audible and sequential rather than textual. This position paper argues for a speech-first view of cultural competence as interactional competence: the ability of a spoken agent to participate appropriately in event-situated interaction with locally normative conduct, while allowing plural acceptable realizations. Here, appropriate does not imply generic human-likeness; in many applications, the desired behavior may instead be constrained, neutral, predictable, or tool-like under an application-specific interaction contract. We synthesize social-science foundations into a theory-derived taxonomy of culture-bearing signals in speech, identify interactional phenomena where transcript correctness fails to predict appropriateness, and ground the agenda in today’s SpeechLM stacks and evaluation practice. We propose an evaluation framing that complements WER/MOS and broad capability suites by making speech events and interaction contracts explicit, diagnosing where modern pipelines lose interactional cues, and treating cultural appropriateness as a norm-conditioned target rather than generic “naturalness.”
Social Aspects · Safety
Large language model (LLM)-powered multi-agent systems (MAS) enable agents to communicate and share information, achieving strong performance on complex tasks. However, this communication also creates an attack surface where malicious agents can propagate misinformation and steer group decisions, undermining MAS safety. Existing embedding-based defenses aim to detect and prune suspicious agents, but their effectiveness depends on a clear separation between the text embeddings of malicious and benign messages. Stronger attackers can break such defenses by crafting messages whose embeddings lie close to benign ones. We analyze this failure mode theoretically and validate it empirically with three stronger attacks, Slow Drift, Benign Wrapper, and Chaos Seeding. Our findings also expose a key limitation of embedding-based defenses: they operate only on the text embeddings and ignore token-level confidence signals such as logits, which can remain informative when embeddings are not distinguishable under attack. In this paper, we use confidence score to prune or down-weight messages during MAS communication. Experiments show improved robustness across models, datasets, and communication topologies. Moreover, we find that the effectiveness of confidence signals decays over communication rounds, highlighting the importance of early intervention. This insights can inform and inspire future work on MAS attacks and defenses.
Social Aspects · Privacy
Federated Learning (FL) faces significant challenges due to domain heterogeneity, where data from different clients exhibit substantial statistical shifts that hinder the generalization of the global model. Although existing methods attempt to mitigate this by exchanging class prototypes, they fall short by representing an entire class's complex distribution with a single point. This oversimplification disregards the rich structural information within the data, especially across diverse domains. To address this limitation, we propose a paradigm shift from point-based representation to structure-based knowledge transfer. We introduce Federated Manifold Learning (FML), a novel framework that leverages perceptual manifolds—the intrinsic geometric structures of classes in the feature space—as rich knowledge carriers. In FML, clients transmit compressed manifolds, which are adaptively fused on the server using an attention-based Manifold Mutual Learning (MML) mechanism. This process enables domain-specific structures to learn from each other, creating a unified yet flexible global convergence target. Manifold-guided local training, enforced by a manifold approximation loss and a separation loss, further aligns local models with this global structure. Extensive experiments on the Digits and Office31 benchmarks demonstrate that FML substantially outperforms state-of-the-art methods, achieving accuracy improvements of up to 6.48%.
Social Aspects · Accountability, Transparency, and Interpretability
Modern AI systems are typically developed through multiple stages-pretraining, fine-tuning rounds, and subsequent adaptation or alignment, where each stage builds on the previous ones and updates the model in distinct ways. This raises a critical question of accountability: when a deployed model succeeds or fails, which stage is responsible, and to what extent? We pose the accountability attribution problem for tracing model behavior back to specific stages of the model development process. To address this challenge, we propose a general framework that answers counterfactual questions about stage effects: how would the model's behavior have changed if the updates from a particular stage had not occurred? Within this framework, we introduce estimators that efficiently quantify stage effects without retraining the model, accounting for both the data and key aspects of model optimization dynamics, including learning rate schedules, momentum, and weight decay. We demonstrate that our approach successfully quantifies the accountability of each stage to the model's behavior. Based on the attribution results, our method can identify and remove spurious correlations learned during image classification and text toxicity detection tasks that were developed across multiple stages. Our approach provides a practical tool for model analysis and represents a significant step toward more accountable AI development.
Social Aspects · Security
While Large Language Models (LLMs) excel in code generation, they remain prone to replicating subtle yet critical vulnerabilities endemic to their training data. Current alignment techniques, such as Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL), typically apply coarse-grained optimization at the sequence level. This approach often fails to address the localized nature of security flaws, where a single incorrect token choice can compromise an entire program. To bridge this gap, we introduce Tree-like Self-Play (TSP), a framework that reframes secure code generation as a fine-grained sequential decision process. Unlike standard methods that blindly maximize likelihood, TSP constructs a decision tree where the model explores branching trajectories—generating both secure "golden paths" and vulnerable variants. By treating code generation as a self-play game, the model learns to strictly discriminate against its own localized errors. This provides a dense, on-policy learning signal that forces self-correction precisely at the critical decision nodes where vulnerabilities typically emerge. Our experiments demonstrate that TSP fundamentally enhances model reliability. In Python security benchmarks, TSP boosts CodeLlama-7B’s pass rate (SPR@1) to 75.8\%, significantly outperforming SFT (57.0\%) and unstructured self-play baselines. Crucially, TSP induces robust out-of-distribution generalization: the model not only reduces vulnerabilities in unseen categories (CWEs) by 24.5\% but also successfully transfers security principles learned from C/C++ to diverse languages, including Python, Go, and JavaScript. This suggests that TSP does not merely memorize patches, but internalizes abstract, language-agnostic security logic.
Social Aspects · Privacy
Modern LLM-powered autonomous agents increasingly rely on rich user interface (UI) state observations to achieve reliable action grounding in complex digital environments. However, many deployments transmit the full UI state to remote inference servers even when most elements are irrelevant to the current task, which can leak sensitive but unnecessary context such as authentication codes, private notifications, and background application states. We propose MINIM, a trusted local broker that performs privacy-aware minimization on the client side before any observation leaves the device. Grounded in Contextual Integrity (CI), MINIM learns a dual-score representation for each UI element by predicting an inherent sensitivity score (s) and a task-conditioned necessity score (n). These scores drive a trinary disclosure policy that keeps essential elements, abstracts sensitive attributes when needed, and removes task-irrelevant content. We optimize a CI-aware objective that penalizes necessity errors more strongly on high-risk content, enabling aggressive pruning while preserving task-critical information. Experiments on real-world UI observations derived from WebArena show that MINIM reduces task-irrelevant sensitive leakage to 1.1% while preserving 79.8% of task-critical semantic context. Moreover, MINIM retains 95.7% of task-critical interactive affordances, indicating that local sanitization can enforce task-conditioned minimization at both the structural and attribute levels while retaining the signals required for agent actions.
Social Aspects · Security
We propose dgMARK, a decoding-guided watermarking method for discrete diffusion language models (dLLMs). Unlike autoregressive models, dLLMs can generate tokens in arbitrary order. While an ideal conditional predictor would be invariant to this order, practical dLLMs exhibit strong sensitivity to the unmasking order, creating a new channel for watermarking. dgMARK steers the unmasking order toward positions whose high-reward candidate tokens satisfy a simple parity constraint induced by a binary hash, without explicitly reweighting the model’s learned probabilities. The method is plug-and-play with common decoding strategies (e.g., confidence, entropy, and margin-based ordering) and can be strengthened with a one-step lookahead variant. Watermarks are detected via elevated parity-matching statistics, and a sliding-window detector ensures robustness under post-editing operations including insertion, deletion, substitution, and paraphrasing.
Social Aspects · Security
Unlearnable examples (UEs) aim to compromise model training by injecting imperceptible perturbations to clean samples. However, existing UE schemes exhibit limited robustness against advanced defenses due to their heuristic design or narrowly scoped domain perturbations. To address this, we propose \texttt{DUNE}, a \underline{\textbf{D}}ual-branch \underline{\textbf{UN}}learnable \underline{\textbf{E}}nsemble perturbation optimization approach. Specifically, \texttt{DUNE} separately optimizes perturbations in the spatial and color domains to establish the mapping between perturbations and shift-induced labels. This design extends the perturbation domain to increase noise intensity for improving robustness and drives the models to learn perturbation-oriented features with degraded generalization, thereby achieving unlearnability. To strengthen \texttt{DUNE}'s performance, we further propose an unlearnability-enhancing ensemble strategy that aggregates diverse pre-trained models during the dual-branch optimization. Extensive experiments on benchmark datasets CIFAR-10 and ImageNet verify that \texttt{DUNE}'s robustness outperforms 12 SOTA UE schemes under 7 mainstream defenses, yielding a lower average test accuracy of 14.95\% to 50.82\%.
Social Aspects · Fairness
Post-hoc controllability of fair machine learning models, the ability to control the trade-off between fairness and accuracy after training, is valuable for practical deployment. Existing post-processing methods provide such post-hoc controllability but often suffer from significant accuracy degradation, whereas in-processing methods achieve efficient trade-offs but require computationally expensive retraining for each change in trade-off ratio. To achieve both post-hoc controllability and efficient trade-offs, we propose a novel fair classification algorithm that learns effective feature representations to improve the trade-off efficiency of post-processing fair classifiers, by a gradient-based optimization approach. Experimental results on real-world datasets demonstrate that our method achieves trade-off efficiency comparable to, or even surpassing, in-processing methods, without requiring any retraining.